The productivity costs of premature mortality in Australia

Increasingly scarce resources within the health care setting have seen economic evaluations become critical tools in informing government funding decisions. While strong methodological principles exist for the measurement and valuation of health care costs, there has been less focus on estimating the productivity related costs of disease. This is despite evidence highlighting the strong impact that these costs have on economic outcomes. Where productivity costs have been estimated previously, limitations in both scope and methodology exist. The purpose of this thesis was to quantify the productivity related costs of all cause premature mortality in Australia using a consistent and rigorous methodology. Estimates of the future working years and personal income forgone due to premature mortality across a number of age, sex, disease and socioeconomic categories were produced. These costs were then applied to determine their impact on the cost effectiveness outcome of a health policy intervention. The foundation of this work was the development of a new microsimulation model, LifeLossMOD. LifeLossMOD was developed to assign a counterfactual life trajectory to each individual that died prematurely in 2003. These trajectories were modelled to 2030 and assumed to reflect a scenario that would have eventuated if an individual’s premature death in 2003 was prevented. The model accounted for individual variability in age, sex and socio-economic status at the time of death in projecting counterfactual events. Premature deaths occurring in 2003 were estimated to account for over 284,000 working years forgone and $13.8 billion in lifetime income forgone, when modelled to 2030. Male deaths accounted for 81% of the total income loss, which was concentrated in individuals aged 35-64 at the time of death. Premature deaths from cancer and cardiovascular disease together accounted for over half of the total impact. Injuries and mental disorders were associated with the highest average loss in income per death. When productivity costs were incorporated into a cost-effectiveness analysis of a health policy intervention and modelled over 25 years, there was a significant improvement in the estimated net financial benefit of the intervention. The results presented here highlight the magnitude of the productivity costs associated with premature mortality, and the distribution of these costs across a number of population and disease subgroups. This may provide valuable information to decision makers allocating resources between competing health priorities. At the broader societal level, these results provide a compelling case for greater investment in population health as a means of contributing to economic growth